IgG4 as a potential biomarker of acute exacerbations in ILD
Bibliographic record
Abstract
Background: Acute exacerbations (AE) of ILD are associated with a detrimental outcome. Data suggest an association of AE-ILD with a stimulation of the immune system. We therefore assessed a potential role of IgG4 as a predictive biomarker for AE-ILD. Methods: The database of our tertiary referral center for ILD was reviewed for IPF and chronic hypersensitivity pneumonitis (cHP) patients (pts) with available data on IgG4. Clinical, and radiological data were retrospectively analyzed. Through ROC analysis a threshold value of IgG4=1.25 g/L was used as the best cut-off point to graph Kaplan-Meier curves for time to first AE. Results: 170 IPF and 172 cHP pts were identified with a mean age of 71.7 years and 66.7 years; FVC 77.4% and 71.1%; DLCO 44.3% and 46.6%. Mean IgG4 values were: 1.00 and 0.95 g/L, and 25.8% of IPF and 18.5% of cHP demonstrated IgG4≥1.25 g/L. Median time to first AE was 366 days in IPF (20.1% of pts, annual incidence 6.7%) and 303 days in cHP(15% of pts,8.6% annual incidence). IPF pts with IgG4≥1.25 g/L had a significant shorter time to first AE (p=0.002), which was similar in cHP patients by trend (p=0.058)(figure). Conclusions: IgG4 may serve as a predictive biomarker for the risk of AE in IPF and cHP. We also suppose that the analysis of biopsy material for lymphoplasmacytic infiltrates may be useful to differentiate pts at major risk of AE. Prospective studies are needed to confirm these results in large cohorts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".